CN102019926B - Predictive energy management control scheme for a vehicle including a hybrid powertrain system - Google Patents

Abstract

A method for controlling a vehicle having a hybrid powertrain includes monitoring vehicle navigation and traffic patterns associated with a predicted travel path. It extends the powertrain instantaneous controller into a predictive control framework, and utilizes previewed traffic and geographic information based on on-board sensing and navigation information. An impending road load is predicted from which a fuel cost factor is optimized under a model predictive control framework. A state-of-charge trajectory is predicted from the impending road load and operation of the hybrid powertrain system is controlled in response thereto.

Description

Be used for the predictive energy management control scheme of the vehicle that comprises hybrid power system

Technical field

The disclosure relates to the control system of the vehicle for having hybrid power system.

Background technology

Statement in this part only provides the background information relevant to the disclosure, and can not form prior art.

Known hybrid power system structure have comprise explosive motor moment of torsion generating apparatus and can be mechanically connected to driving device to transfer torque to the torque machine of output link.Known torque machine is power by the transformation of energy of storage, to produce moment of torsion.Known hybrid power system comprises an explosive motor for the input link that is connected to (compound-split) double mode, compound separation, electro-mechanical transmission, and this is double mode, compound separation, electro-mechanical transmission has the transmission system that is connected to power actuated vehicle by operability for use in tractive torque being delivered to the output link on it.Torque machine (it comprises the motor that is operating as electrical motor or electrical generator) can not rely on from the moment of torsion of explosive motor input and produces the moment of torsion that is input to change-speed box.Motor can be converted to the electric energy that can be stored in electrical energy storage device by the kinetic energy of the vehicle transmitting by vehicle transmission system.Control system monitoring is from vehicle and operator's various inputs, and provide the operation control to hybrid power system, comprise and control range state and gearshift, the control torque generation device of change-speed box and regulate the electric power exchange between electrical energy storage device and motor, thereby the output of management change-speed box, comprises moment of torsion and rotating speed.

Known comprises execution power management scheme for operating the control policy of hybrid power system, the relevant selected objective target of use of the energy to reach the request to output torque to consumption of fuel, discharge and operation response person to storage.Be used for known power Managed Solution monitoring the present situation of the operation of controlling hybrid power system and produce instant control signal to control the actuator of power system.

Known Vehicular system comprises global positioning system (GPS) and Digital Map System, with monitor vehicle position with respect to the motion of highway system.

Summary of the invention

A kind of vehicle that is equipped with hybrid power system, described hybrid power system comprises the explosive motor of the input link that is operatively connected to hybrid gearbox, described hybrid power system have can operate in case with the torque machine of energy storing device exchange power.Hybrid gearbox is configured to transmitting torque between input link, torque machine and output link.Comprise for the method that operates vehicle: monitor the automobile navigation relevant to the prediction travel path of described vehicle and transit mode (traffic patterns); Predict the imminent road load relevant with described transit mode to described automobile navigation; Estimate the vehicle propulsion power relevant to the imminent road load of described prediction; Determine the charge condition track of the expectation of described energy storing device; Predict the charge condition track of the described energy storing device corresponding with the charge condition track of the vehicle propulsion power of estimation and the expectation of described energy storing device; Control the operation of described hybrid power system with the charge condition track of the prediction in response to described energy storing device.

The present invention also provides following scheme:

1. 1 kinds of schemes are for operating the method for the vehicle that has comprised hybrid power system, described hybrid power system comprises the explosive motor of the input link that is operatively connected to hybrid gearbox, comprise can operate in case with the torque machine of energy storing device exchange power, described hybrid gearbox is configured to transmitting torque between described input link, described torque machine and output link, and described method comprises:

Automobile navigation and transit mode that monitoring is relevant to prediction travel path for described vehicle;

Predict the imminent road load relevant with transit mode to described automobile navigation;

Estimate the vehicle propulsion power relevant to the imminent road load of described prediction;

Be identified for the charge condition track of the expectation of described energy storing device;

Prediction is for the charge condition track of described energy storing device, and it is corresponding to the vehicle propulsion power of described estimation with for the charge condition track of the expectation of described energy storing device; With

Control the operation of described hybrid power system in response to the charge condition track of the prediction for described energy storing device.

The method of scheme 2. as described in scheme 1, further comprises:

Determine the boundary condition relevant to the charge condition track of the prediction for described energy storing device; With

Control the operation of described hybrid power system in response to the output torque of ordering and the described boundary condition relevant to the charge condition track of the prediction for described energy storing device.

The method of scheme 3. as described in scheme 1, further comprise and carry out load prediction control program, with the prediction imminent road load relevant to automobile navigation and transit mode, thereby estimate the vehicle propulsion power relevant to prediction travel path for described vehicle.

The method of scheme 4. as described in scheme 3, described load prediction control program is carried out rolling time domain control program, to predict the imminent road load relevant with transit mode to described automobile navigation.

The method of scheme 5. as described in scheme 4, further comprises execution model predictive control scheme, so that:

Optimize the fuel relevant to described imminent road load and expend coefficient; With

Fuel based on described optimization expends coefficient and dynamically regulates the charge condition track for the prediction of described energy storing device.

The method of scheme 6. as described in scheme 5, wherein, thereby described fuel expend coefficient optimised make fuel in described rolling time domain expend with total power consumption at least one minimize.

The method of scheme 7. as described in scheme 6, wherein, described Model Predictive Control scheme comprises the quasi-static model based on power of total hybrid power system loss.

The method of scheme 8. as described in scheme 1, further comprise and determining at the power stage from described driving engine and from the preferred power division between the power stage of described torque machine, the charge condition track of the prediction of described preferred power division based on for described energy storing device.

The method of scheme 9. as described in scheme 1, wherein, the charge condition track of described expectation comprises electric quantity consumption track.

The method of scheme 10. as described in scheme 1, wherein, the charge condition track of described expectation comprises that electric weight keeps track.

11. 1 kinds of schemes are for operating the method for the vehicle that has comprised hybrid power system, described hybrid power system comprises the explosive motor of the input link that is operatively connected to hybrid gearbox, comprise can operate in case with the torque machine of energy storing device exchange power, described hybrid gearbox is configured to transmitting torque between described input link, described torque machine and output link, and described method comprises:

Automobile navigation, transit mode and operator's driving model that monitoring is relevant to prediction travel path for described vehicle;

Monitoring is inputted relevant operator's torque request to the operator who is input to for the operator interface of described vehicle;

Prediction and described automobile navigation, transit mode, the imminent road load that operator's driving model is relevant with described operator's torque request;

Estimate the vehicle propulsion power relevant to the imminent road load of described prediction;

Be identified for the charge condition track of the expectation of described energy storing device;

Prediction is for the charge condition track of described energy storing device, and it is corresponding to the vehicle propulsion power of described estimation with for the charge condition track of the expectation of described energy storing device; With

Control the operation of described hybrid power system in response to the charge condition track of the prediction for described energy storing device and described operator's torque request.

The method of scheme 12. as described in scheme 11, further comprises:

The fuel of estimating the prediction relevant to the vehicle propulsion power of described estimation expends coefficient; With

Determine to the fuel of described prediction expend coefficient relevant at the power stage from described driving engine with from the preferred power division between the power stage of described torque machine.

The method of scheme 13. as described in scheme 12, further comprises:

Determine to power stage from described driving engine and expend coefficient from the relevant instantaneous fuel of the described preferred power division of carrying out between the power stage of described torque machine; With

Wherein, determine that also further to expend coefficient relevant to described instantaneous fuel at the power stage from described driving engine with from the described preferred power division between the power stage of described torque machine.

14. 1 kinds of schemes, for operating the method for the vehicle that has comprised hybrid power system, comprising:

Predict the travel path of described vehicle;

Monitor automobile navigation and the transit mode relevant to the travel path of described prediction;

Predict the imminent road load relevant with transit mode to described automobile navigation, described automobile navigation is relevant to the travel path of described prediction with transit mode;

Estimate the vehicle propulsion power relevant to the imminent road load of described prediction;

Determine the charge condition track of the expectation of the energy storing device that is configured to the power that is provided for vehicle propulsion;

Prediction is for the charge condition track of described energy storing device, and it is corresponding to the vehicle propulsion power of described estimation with for the charge condition track of the expectation of described energy storing device; With

Control the operation of described hybrid power system in response to the charge condition track of the prediction of described energy storing device.

The method of scheme 15. as described in scheme 14, further comprises:

Determine the boundary condition relevant to the charge condition track of the prediction of described energy storing device; With

Control the operation of described hybrid power system in response to the charge condition track of output torque, the described prediction of order and the described boundary condition relevant to the charge condition track of the prediction of described energy storing device.

The method of scheme 16. as described in scheme 14, further comprises:

Carry out load prediction control program, to predict the described imminent road load relevant with transit mode to described automobile navigation, so that the estimation described vehicle propulsion power relevant to prediction travel path for described vehicle.

The method of scheme 17. as described in scheme 16, wherein,

Described load prediction control program is carried out rolling time domain control program, to predict the described imminent road load relevant with transit mode to described automobile navigation.

The method of scheme 18. as described in scheme 17, further comprises execution model predictive control scheme, so that:

Optimize the fuel relevant to described imminent road load and expend coefficient; With

Fuel based on described optimization expends coefficient dynamically adjusts the charge condition track of the prediction of described energy storing device.

The method of scheme 19. as described in scheme 14, wherein, the charge condition track of described expectation comprises electric quantity consumption track.

The method of scheme 20. as described in scheme 14, wherein, the charge condition track of described expectation comprises that electric weight keeps track.

Brief description of the drawings

With reference to accompanying drawing, will one or more embodiment be described in the mode of example, in the accompanying drawings:

Fig. 1 is the schematic diagram according to exemplary hybrid power system of the present disclosure;

Fig. 2 is according to the schematic diagram of the exemplary architecture of the control system of exemplary hybrid power system of the present disclosure;

Fig. 3 is the graph of relation that consumes the charge condition of the factor and energy storing device according to preferred fuel of the present disclosure; With

Fig. 4 A～4D is according to the diagram of curves that operates control program in hybrid power system of the present disclosure.

Detailed description of the invention

Referring now to accompanying drawing, it is the object for some exemplary embodiment is described only, is not limited to the object of these exemplary embodiments, the schematically illustrated vehicle 10 that comprises control system 100, hybrid power system 200 and transmission system 300 of Fig. 1.

In one embodiment, transmission system 300 can comprise the compensating gear 310 that is mechanically connected to axletree 320 or semiaxis, and axletree 320 or semiaxis are mechanically connected to wheel 330 again.Compensating gear 310 is coupled to the output link 64 of hybrid power system 200.Transmission system 300 is by wheel 330 transmitting tractive power between hybrid gearbox 250 and road surface.

Hybrid power system 200 comprises energy storing device (ESD) 210, and energy storing device (ESD) 210 is stored potential energy and is coupled to one or more torque machines 230, with transmitted power between them.In the time that ESD210 comprises that electrical energy storage device and torque machine 230 comprise motor/generator, controlled power inverter can be placed between ESD210 and torque machine 230, and for changing electric power.Torque machine 230 can operate taking by the transformation of energy of storage as mechanical horsepower, and can operate mechanical horsepower to be converted to the energy that can be stored in ESD210.Driving engine 240 can operate that the fuel being stored in Fuel Tank 220 is converted to mechanical horsepower.Can be passed to hybrid gearbox 250 and torque machine 230 from the mechanical horsepower of driving engine 240.Can be passed to hybrid gearbox 250 and driving engine 240 from the mechanical horsepower of torque machine 230.Can be passed to driving engine 240, torque machine 230 and be delivered to transmission system 300 by output link 64 from the mechanical horsepower of hybrid gearbox 250.The mechanical horsepower being passed can be the form of tractive torque and the form of the reactive torque of the car brakeing for relevant to regenerative braking capability for vehicle propulsion.

Preferably; driving engine 240 optionally operates in multiple states, comprises a state in a state in engine behavior and engine shutdown state, full cylinder state and cylinder dead status and supplies a state in fuel state and fuel cut off state.Preferably, hybrid gearbox 250 optionally operates in a range state in multiple range states, comprises the range state of fixing gear and continuous variable.Torque machine 230, driving engine 240 and hybrid gearbox 250 respectively comprise multiple for monitoring the sensing device of its operation and for controlling the actuator of its operation.

Alternately, hybrid power system is relevant to elec. vehicle, and this elec. vehicle comprises the elec. vehicle of the range capability with expansion.Hybrid power system comprises torque machine 230, it is for being mechanical torque by the transformation of energy that is stored in energy storing device (ESD) 210, so that for vehicle propulsion, described mechanical torque comprises for the tractive torque of vehicle propulsion and the reactive torque relevant to regenerative brake by transmission system 300.A torque machine (or multiple torque machine) 230 is operatively coupled to explosive motor 240, mechanical torque is converted to the potential energy that can be stored in energy storing device (ESD) 210.

Control system 100 comprises control module 120, and it is connected to operator interface 130 and is connected to GPS/ communication system 110 by signal ground by signal ground.GPS/ communication system 110 preferably includes extra vehicle communication capability and three-dimensional (3D) geographic information services (GIS) Digital Map System, so that sea level elevation information to be provided, this sea level elevation information is for the traffic route relevant to the prediction travel path of vehicle 10.GPS/ communication system 110 can comprise vehicle-mounted inertia measurement sensor, infrared sensing device, radar, laser radar and other monitoring system (these all do not illustrate), so that the vehicular communication pattern of monitoring and the instant location of estimation.Operator interface 130 comprises multiple people/machine interface arrangements, by these people/machine interface arrangements, vehicle operators is carried out order to the operation of vehicle 10, and these people/machine interface arrangements comprise acceleration pedal, brake pedal and change-speed box scope finder (PRNDL).

Vehicle operators transmits output torque order by acceleration pedal, brake pedal and change-speed box scope finder, this output torque order comprises the preferable range state of operator's torque request, vehicle direct of travel (, advance or retreat) and hybrid gearbox 250.Operator interface 130 can further comprise the interface that leads to onboard navigation system, and this onboard navigation system and GPS/ communication system 110 interact.

Control module 120 is connected in torque machine 230, driving engine 240, hybrid gearbox 250 and ESD210 the sensing device of each by signal, to monitor its operation and to determine its parameter state.In the time that ESD210 comprises electrical energy storage device, the state of the ESD210 monitoring preferably includes instantaneous current and temperature.Control module 120 is calculated and has been represented that ESD210 is delivered to power the parameter state of the ability of torque machine 230.In the time that ESD210 is electrical energy storage device, the parameter state of ESD210 comprises charge condition (SOC).The state of the driving engine 220 of monitoring preferably includes engine speed (N _e), output torque (T _e) or load and temperature.The state of the hybrid gearbox 250 of monitoring preferably includes rotative speed and the hydraulic pressure in multiple positions, can determine and comprise that concrete moment of torsion transmits the parameter state of the applicable cases of power-transfer clutch from these states.The state of the torque machine 230 of monitoring preferably includes speed and power stream, and for example electric current, can determine the parameter state from the output motor moment of torsion of torque machine 230 from these states.

Control module 120 is operatively coupled to each the actuator in torque machine 230, driving engine 220 and hybrid gearbox 250, to control its operation according to performed control program, wherein said control program is stored with algorithm and calibration procedure form.The actuator associated with torque machine 230 preferably includes converter module.The actuator associated with driving engine 220 preferably includes for example fuel injector, air flow controller, spark ignition system and operates other associated known devices of (comprising control engine state) with control engine.The actuator associated with hybrid gearbox comprises for activating moment of torsion and transmits power-transfer clutch to realize the spiral piping arrangement of operation of concrete range state.

Control module 120 preferably includes one or more general purpose digital computers, and each general purpose digital computer comprises: microprocessor or central processing unit; Comprise the storage medium of read-only memory (ROM) (ROM), random access memory (RAM), EPROM (EPROM); High-frequency clock; Modulus (A/D) change-over circuit and digital-to-analogue (D/A) change-over circuit; And input/output circuitry and device (I/O) and suitable Signal Regulation and buffer circuit.Control module 120 has one group of control algorithm, comprises in one that is stored in storage medium and is performed resident program instructions and the calibration procedure of the function that expectation is provided.The information that is passed to control module 120 and transmit from control module 120 can realize by the mode of direct connection, local area network bus and series connection peripheral interface bus.The algorithm of control program is performed in default repetition period, and each algorithm is performed at least once in each repetition period.The algorithm being stored in Nonvolatile memory devices is carried out by central processing unit, to monitor input and execution control routine and the diagnostics routines from sensing device, thereby control the operation of the actuator being associated with the element of hybrid power system 200 with calibration procedure.Repetition period is performed with regular interval, for example, carry out with every 3.125,6.25,12.5,25 and 100 milliseconds in ongoing operating period of hybrid power system.Alternately, algorithm can be carried out in response to the generation of event.

Fig. 2 shows the key element of the predictive energy management control scheme of hybrid power system for the controlling vehicle hybrid power system 200 of the vehicle 10 shown in Fig. 1 (for example, for).This predictive energy management control scheme comprises: the automobile navigation that has comprised the traffic route relevant to the prediction travel path of vehicle 10 is monitored; And, monitoring operator driving model.The on the horizon vehicle propulsion power request relevant with operator's driving model to automobile navigation predicted iteratively, preferably included the imminent road load that the prediction travel path to having considered automobile navigation and operator's driving model is associated and predict.Comprise load prediction control program (road load prediction) 410, Model Predictive Control scheme (predictive controller) 420 and powertrain control scheme (hybrid power system controller) 430 for the predictive energy management control scheme that operates motor vehicle driven by mixed power 10.

Load prediction control program 410 comprises that vehicle operating parameter to having comprised current automobile navigation, transit mode and operator's driving model and the state of ambient parameter monitor.The imminent road load (RL) that the automobile navigation that 410 predictions of load prediction control program are corresponding with the prediction travel path of vehicle 10 and transit mode are associated.The vehicle propulsion power request that Model Predictive Control scheme 420 use are relevant to imminent road load is carried out predict fuel and is expended coefficient lambda _pred, in one embodiment, this fuel expends coefficient lambda _predpreferred power-division ratios between dictate engine 240 and torque machine 230.To expend coefficient based on vehicle propulsion power request with preferred fuel to determine for the preferred operations state of hybrid power system, to reach preferred fuel efficiency.Powertrain control scheme 430 is controlled the operation of hybrid power system under preferred operations state, so that response vehicle propulsion power request and the use a model predictive control scheme 420 definite predict fuel relevant to imminent road load expends coefficient lambda _pred.For ease of describing, it is the key element separating that load prediction control program 410, Model Predictive Control scheme 420 and powertrain control scheme 430 are shown as.But should be realized that, function described and that carried out by breakaway-element can be used one or more devices to carry out, and comprises the algorithm in one or more control modules for example, predetermined calibration procedure, hardware and/or special IC (ASIC).

One of operating parameter of hybrid power system 200 comprises the expectation charge condition track (the SOC track of expectation) of ESD210, and this comprises the one in electric weight maintain strategy and electric quantity consumption strategy.This control program can be predicted the relevant SOC scope of charge condition track and energy storing device, and this SOC scope expends the expectation charge condition track of coefficient and energy storing device corresponding to the estimation fuel relevant to the imminent road load of prediction.

Load prediction control program 410 comprises vehicle-state and comprises that the ambient parameter of current automobile navigation, transit mode and operator's driving model monitors.Control module 120 is monitored from the signal of the GPS/ communication system 110 that has comprised any available extra vehicle communication device and vehicle-mounted monitoring system and is inputted, to assess transit mode and to predict car speed.This information comprises the input from GPS device, inertia measurement sensor and infrared pickoff and radar installation, so that the traffic information near the active window of restriction estimation vehicle.

By inserting with the vehicle-mounted 3D GIS numerical map of gps signal input and the car speed of prediction, determine sea level elevation and the terrain information of vehicle, to produce navigation path.This comprises the gps system that has inserted the information in 3DGIS numerical map, to provide sea level elevation information for the prediction travel path of vehicle 10.Out of Memory comprises the current transit mode around of geographic position of current geographic position, vehicle 10 of vehicle 10 and the current route of vehicle 10, the direction that this vehicle is advanced on the concrete road of its current line inbound path.The geography information of current route is evaluated, to determine the road load impact relevant to the topographic change of sea level elevation, bend, four corners and other roadway characteristic.

Operator's driving model preferably includes the ratio between the average power requirement that uses operator interface 130 definite, average B.P. demand, the standard deviation of driving power demand and vehicle stop time and total driving time.Based on the driving cycle information in vehicle 10 available statistics of ongoing operating period, use driving model recognition function can predicted operation person's driving model.This preferably includes monitoring operator driving model, to drive from history the driving model information that obtains statistics cycle information.

Load prediction control program 410 is predicted the upper imminent road load relevant to automobile navigation, operator's driving behavior, vehicle sea level elevation and terrain information and transit mode of the rolling time domain (receding horizon) of the travel path in prediction iteratively with periodically.Imminent road load is preferably taked the horsepower output request (P of prediction _pred) and prediction car speed (V _ss-pred) form.Rolling time domain is the time window of follow-up two to three minutes of vehicle operating, and during this time window, imminent road load can be predicted based on above-mentioned input.

The vehicle propulsion power request of Model Predictive Control scheme 420 based on relevant to imminent road load determined the control of hybrid power power management, and its fuel that is represented as prediction expends coefficient lambda _pred.Model Predictive Control scheme 420 is used the vehicle propulsion power request relevant to the vehicular drive situation of imminent road load and prediction, determine iteratively preferred power management control, comprise the power division ratio between driving engine 240 and torque machine 230, the fuel that the predictive control that preferably uses a model framework produces prediction expends coefficient lambda _pred.

The preferred input of the Model Predictive Control scheme 420 that is used for determining the preferred power management control relevant to imminent road load comprises the preferred trajectories (the SOC track of expectation) of the charge condition of ESD210, current car speed and the range state for hybrid gearbox 250, comprises having online and the execution of the gearshift figure (shift map) planning in advance of real time modifying.Other input comprises current dynamical system serviceability, it comprise output speed, the ESD210 of output torque order, hybrid gearbox 250 SOC, the watt loss in driving engine 240 is embodied as the driving engine 240 of the function of operating speed and load efficiency chart, the watt loss in hybrid gearbox 210 is embodied as conventionally to efficiency chart and the system restriction condition of the hybrid gearbox 210 of the function of operating speed.System restriction condition comprise due to transmit with moment of torsion that the relevant restriction of power-transfer clutch causes to passing through the minimum and maximum restriction, the Power Limitation of ESD210, the motor torque capacity of torque machine 230 of moment of torsion of hybrid gearbox 210, etc.The SOC of ESD210 is characterised in that these aspects: minimum and maximum boundary condition (BAT SOC Max, BAT SOC Min) and the SOC scope (+/-Δ SOC) of current SOC, battery charging state.

The range state of hybrid gearbox 250 is inputs of the predetermined gearshift figure that constructs of the offline optimization by using the predetermined driving cycle.Predetermined gearshift figure was modified based on actual range state in ongoing operating period, and this actual range state is responded during the practical operation of hybrid power system by hybrid power system controller 430 and expends to imminent road load and fuel the vehicle propulsion power request that coefficient is relevant and determine and realize.

Model Predictive Control scheme 420 is carried out iteratively, and the fuel that minimizes the prediction that fuel and/or gross horsepower expend to be created in rolling time domain under periodic time step expends coefficient lambda _pred.The minimum and maximum boundary condition of the SOC of ESD210 has been indicated the ability of ESD210 transmitted power, comprises charge power and discharge power.The charge condition track of ESD210 is relevant in electric weight maintain strategy and electric quantity consumption strategy one.Electric weight maintain strategy comprises operation hybrid power system 200, thereby when the SOC of ESD210 in the time that journey finishes is started with journey, the SOC of ESD210 is substantially the same.SOC scope (+/-Δ SOC) can change during journey.Electric quantity consumption strategy comprises operation hybrid power system 200, is the SOC of ESD210 is little while starting than journey predetermined SOC value or in its vicinity thereby make the SOC of ESD210 in the time that journey finishes.In one embodiment, minimum and maximum boundary condition and SOC scope (+/-Δ SOC) are the basic static border not changing in time.In one embodiment, minimum and maximum boundary condition is included in the scope between 60% and 50% SOC.In another embodiment, minimum and maximum boundary condition is the dynamic boundary of following electric quantity consumption track.

For real-time execution, Model Predictive Control scheme 420 is constructed to retrain double optimization problem, with the calculated amount of minimum restriction nonlinear optimal problem.

Model Predictive Control scheme 420 is supposed the prediction horsepower output request (P in response to output torque order _pred) be the power by using driving engine 240 to produce and the storage power (P utilizing from ESD210 _eSD) power (P that produced by torque machine 230 _eng) realize, to tractive output is transferred to transmission system 300.Total consumption of power comprises corresponding total system power loss (P _lossTotal):

P _Pred=P _Eng+P _ESD+P _LossTotal [1]

Minimum total system power loss (P _lossTotal) determined as follows.Vehicle and dynamical system are represented as the quasistatic math modeling based on power, its enough accurately in case the system performance modeling to all expectations can calculate in real time simultaneously.Total system power loss (P _lossTotal) be defined as like that as follows the combination from the loss in efficiency of the key element of hybrid power system:

P _LossTotal=λ·P _LossEng+P _LossESD+P _LossMech+P _LossMot [2]

Wherein λ is that fuel expends coefficient.

Loss in efficiency (the P of driving engine 240 _lossEng) by determined as follows:

$P_{\mathrm{LossEng}}={\mathrm{\α}}_2\left(N_E\right)\·P_{\mathrm{Eng}}^2+{\mathrm{\α}}_1\left(N_E\right)\·P_{\mathrm{Eng}}+{\mathrm{\α}}_0\left(N_E\right)---\left[3\right]$

Wherein α ₂, α ₁and α ₀for predetermined constant, N _efor the rotating speed of driving engine.

Loss in efficiency (the P of torque machine 230 _lossMng) by determined as follows:

$P_{\mathrm{LossMot}}={\mathrm{\γ}}_2{\left(\mathrm{\ω}\right)}_{\mathrm{Mot}}^2+{\mathrm{\γ}}_1\left(\mathrm{\ω}\right)\·P_{\mathrm{Mot}}+{\mathrm{\γ}}_0\left(\mathrm{\ω}\right)---\left[4\right]$

Wherein γ ₂, γ ₁and γ ₀for predetermined constant, ω is the rotating speed of torque machine 230.

Loss in efficiency (the P of ESD210 _lossESD) by determined as follows:

$P_{\mathrm{LossESD}}={\mathrm{\β}}_2(\mathrm{SOC},\mathrm{Temp})\·P_{\mathrm{ESD}}^2---\left[5\right]$

Wherein β ₂for predetermined constant, the temperature that Temp is ESD210, the current charge condition that SOC is ESD210.

Mechanical horsepower loss (the P of hybrid gearbox 250 _mechLoss) as follows:

$P_{\mathrm{MechLoss}}=a{N}_I+b{N}_I^2+c{N}_I{N}_O+d{N}_O^2---\left[6\right]$

Wherein N _ifor the rotating speed of the input shaft of hybrid gearbox 250, power is transmitted from driving engine 240 by this input shaft, N _ofor the rotating speed of the output link 64 of hybrid gearbox 250.Although N _iand N _ecan be owing to being placed on damper between driving engine 240 and input shaft and different, but N _iand N _ebe considered to equate under stabilized conditions, as follows:

N _E=N _I [7]

Motor speed omega, change-speed box input speed N _iwith change-speed box output speed N _othere is following kinetics relation:

$\mathrm{\ω}=\left[\begin{array}{cc}{s}_1& s_2\end{array}\right]\·\left[\begin{array}{c}{N}_I\\ N_o\end{array}\right]---\left[8\right]$

Wherein s ₁and s ₂for predetermined constant.

Nonlinear component model be approximately on the horizon and the power that closes to an end between secondary relation, and be reduced to total system power loss (P of total _lossTotal) single formula, as follows:

P _LossTotal=P(λ _pred，Range State,T _E) [9]

Therefore, total system power loss P of total _lossTotalbecome the secondary cost function (quadratic cost function) that has omitted high-order term, wherein, on the time period of rolling time domain N, λ _predfor the fuel of prediction expends coefficient, Range State is the range state of hybrid gearbox 250, T _efor the input torque from driving engine 240 to hybrid gearbox 250.Fuel expends coefficient lambda, and to be assumed to be at the time period N of rolling time domain upper be constant.

Therefore, nonlinear optimal problem can be reduced to constraint double optimization problem, and it will obtain the total system power loss (P adding up to _lossTotal) global minimum.Expend coefficient lambda for the fuel of predicting _predpreferred value and the range state of hybrid gearbox 250 can be determined, T _efor the input torque from driving engine 240, it can be determined within relatively short computing time.

In a word, double optimization problem predict fuel expends coefficient lambda, and it obtains the minimum loss in efficiency for the predicted link load in rolling time domain N, and has dynamically determined the SOC border in rolling time domain N.Fuel expends coefficient lambda _kpreferably include like that as follows in multiple preset values:

λ _k∈(λ ₁，λ ₂，…，λ _m) [10]

Wherein λ ₁, λ ₂, λ _mcomprise the numeral in 0 to 1.

When supposition fuel expends while being the constant with predetermined discrete value in each time period N of coefficient lambda in rolling time domain, on-line optimization problem reduction is that the fuel of prediction is expended to coefficient lambda _predbe chosen as on rolling time domain N about sampling time Δ T _srealize the λ of minimum predicted power loss _kpreset value, as follows.

$\left({\mathrm{\λ}}_k\right)=\underset{{\mathrm{\λ}}_k}{\arg \min }\underset{i=0}{\overset{N}{\mathrm{\Σ}}}\mathrm{\Δ}{T}_s\·P_{\mathrm{LossTotal}}(k+i|k)---\left[11\right]$

Thereby change-speed box range state is determined in advance by the preplanned gearshift figure that includes online modification, Model Predictive Control scheme 420 is carried out with relatively low speed, for example cycle time of 1 second.In rolling time domain N, reach the λ of minimum predicted power loss _kpreset value be designated as for the predict fuel of powertrain control scheme 430 and expend coefficient lambda _pred.

In the situation that considering current SOC and SOC scope, powertrain control scheme 430 is controlled the operation of hybrid power system under preferred serviceability, to produce the moment of torsion to transmission system 300 and the speed output in response to vehicle propulsion power request, the fuel of this vehicle propulsion power request and imminent road load and prediction expends coefficient lambda _predrelevant.SOC scope is limited by the static or dynamic minimum and maximum boundary condition of SOC.

The fuel that has comprised prediction is expended coefficient lambda by Model Predictive Control scheme 420 _predsend powertrain control scheme 430 to the control variable of the prediction SOC track of ESD210.Other input comprises to the minimum and maximum boundary condition of the SOC track SOC relevant with SOC scope (SOC Range (+/-Δ SOC)) of prediction, for the minimum and maximum boundary condition (BAT SOC Max, BAT SOC Min) of battery charging state and the current SOC of ESD210.Other input comprises power operation, and it comprises engine speed (N _e) and engine output, for example engine torque (T _e).Other input comprises the current operation range state (Range State) of hybrid gearbox 200.Other input comprises current dynamical system serviceability, it comprise output torque order, hybrid gearbox 250 output speed, the loss in efficiency of driving engine 240 is embodied as the driving engine 240 of the function of operating speed and load efficiency chart, the loss in efficiency of hybrid gearbox 210 is embodied as to efficiency chart and the system restriction of the hybrid gearbox 210 of the function of common operating speed.System restriction comprise due to transmit with moment of torsion that the relevant restriction of power-transfer clutch causes to passing through the minimum and maximum restriction, the Power Limitation of ESD210, the motor torque capacity of torque machine 230 of moment of torsion of hybrid gearbox 210, etc.Powertrain control scheme 430 is determined the preferred operations point of driving engine 240 and torque machine 230, and this preferred operations point is subject to the restriction of above-mentioned system restriction and current dynamical system serviceability.

Powertrain control scheme 430 is used for stablizing the dynamic behaviour between driving engine 240 and torque machine 230.This comprises and uses from the definite target SOC of the prediction SOC track of ESD210 and use the minimum and maximum boundary condition of the SOC relevant to prediction SOC track (SOC Range (+/-Δ SOC)) from Model Predictive Control scheme 420 to expend coefficient lambda to determine instantaneous fuel _instsOC control function.This illustrates with reference to Fig. 3.In the following manner, the fuel based on prediction expends coefficient lambda _predexpend coefficient lambda with instantaneous fuel _instthe fuel of determining combination expends coefficient lambda _comb.

λ _comb=λ _pred+λ _inst [12]

The fuel of combination expends coefficient lambda _combcan be used by policy optimization function, to determine in real time preferred change-speed box range state and engine speed.The fuel of combination expends coefficient lambda _combbe used to dynamically determine to apply substituting engine operation state, comprise cylinder deactivation serviceability and fuel cut off state.Can also determine the preferred torque distribution between driving engine 240 and torque machine 230.

Powertrain system 200 can comprise: for example in the time that vehicle 10 is static (for example, while stopping a period of time), can be used to the external power supply into ESD210 charging.Control module 120 can comprise the operation strategy of the electric quantity consumption strategy that comprises ESD210.In this embodiment, minimum and maximum boundary condition is the dynamic boundary of following in time preferred electric quantity consumption strategy.Powertrain system 200 is considered electric quantity consumption strategy, manages the power stage of ESD210 and driving engine 240.

Powertrain control scheme 430 is used the system loss (P of the total of hybrid power system _{lossTotalInst}), being formulated the minimum power loss optimization of controlling for instantaneous power management, it has determined the preferred power division between driving engine and electrical motor, as follows:

P _{lossTotalInst}=λ _combp _lossEngInst+ P _lossESDInst+ P _lossMechInst+ P _lossMotInst[13] P wherein _lossEngInst, P _lossESDInst, P _lossMechInstand P _lossMotInstfor the instantaneous system loss in efficiency of driving engine 240, ESD210, change-speed box 250 and the torque machine 230 that can be determined empirically.

By determining feasible operational space for the dynamical system operating parameter of selecting in advance such as the horsepower input from driving engine (Ti/Ni), and search and the minimum system loss in efficiency (P in real time operation _{lossTotalInst}) the feasible operational space of corresponding operating point, identify relevant to preferred engine/motor power division for the driving engine 240 of hybrid gearbox 250 and the operating point of torque machine 230.Owing to comprising that in the constraint nonlinear optimal problem being formulated the fuel of combination expends coefficient lambda _combso, to the search of minimum system loss in efficiency be modified in case as to use relevant parameter and with the described selection of setovering of can the recharge/discharge cycle relevant electric current output of ESD210.

Comprise three layers of instantaneous optimization for the powertrain control scheme 430 of controlling hybrid power system 200, to determine the preferred operations point of hybrid power system 200.Ground floor is carried out, and to determine hybrid power range state and engine speed, the second layer is carried out, and to determine engine torque and engine operation state, the 3rd layer of execution, to determine motor torque.These operating points will be realized by the coordination control of electrical generator 240, change-speed box 250 and torque machine 230 subsequently.This determines constraint manipulation space and current change-speed box range state during being included in each iteration, to determine the preferred engine speed (N of expectation _e), the preferred input torque (T of expectation from driving engine 240 to change-speed box 250 _e), comprised and the relevant clutch moment of torque (T of expectation is carried out in gearshift _cL) any change hybrid gearbox 250 preferable range state (the Range State of expectation) and from the preferred motor torque (T of expectation of torque machine 230 _m) output.

The operation of hybrid power system 200 in response to comprised operator's torque request and ESD210 prediction charge condition track output torque order and controlled.Comprise the upcoming power request that the hybrid power system 200 of engine condition and hybrid gearbox range state preferred operations state used can be based on prediction and determined for the preferred power management control of vehicle propulsion.Engine condition comprises engine operation and engine shutdown state, full cylinder state and cylinder dead status and supply fuel and fuel cut off state.Change-speed box range state comprises continuous variable pattern and fixing gear range state.

Show the horsepower output request P of minimum and maximum border (BAT SOC Max, BAT SOC Min) according to the battery charging state of control program described herein, the prediction relevant to imminent road load (RL) Fig. 4 A to Fig. 4 D figure _pred, prediction fuel expend coefficient lambda _pred,, prediction SOC track and the SOC scope (+/-Δ SOC) of drawing with respect to rolling time domain.The SOC track of expecting is preferably predetermined, comprises border, the SOC track that this border is included in expectation admissible SOC opereating specification (+/-Δ SOC) around.Dynamically definite SOC border can allow opereating specification (+/-Δ SOC) to be set according to the SOC of the SOC track with respect to prediction.

The SOC track of expecting comprises the desirable time rate of change in the SOC of the ESD210 relevant to the operation of vehicle 10.For having adopted the vehicle that operates in the hybrid power system under electric weight Holdover mode, the SOC track of expectation tends to that the SOC of ESD210 is controlled to the original SOC value occurring when the SOC in the time that journey finishes is started with journey and substantially equates.For having adopted the vehicle that operates in the hybrid power system under electric quantity consumption pattern, the SOC track of expectation tends to the SOC of ESD210 to be controlled to and to make the SOC of ESD210 in the time that journey finishes reach the minimum SOC value of target, to minimize the consumption of onboard fuel.The SOC track of the exemplary expectation in electric quantity consumption operation is that the linearity between the minimum SOC of initial SOC and target connects.

Imminent road load (RL) is predicted iteratively and periodically by usage load predictive control scheme 410.The fuel of prediction expends coefficient (λ _pred) be contemplated to imminent road load (RL) and be inversely proportional to.The fuel of this prediction expends coefficient and is used to determine the prediction SOC track with direct relation.Therefore, the increase that the fuel of prediction expends coefficient will increase prediction SOC track, thereby allow ESD210 to have stronger rate of discharge at once, and the fuel of prediction expends the SOC track that reduces to reduce prediction of coefficient, thereby limit the rate of discharge at once of ESD210.

Model Predictive Control scheme 420 is carried out, and expends coefficient lambda with based on fuel _preddetermine dynamic SOC opereating specification, as described in reference to Fig. 2.SOC is controlled in the fuel of predicting and expends coefficient lambda _predin the SOC track of corresponding prediction dynamic definite SOC border (+/-Δ SOC) around.The fuel of prediction expends coefficient lambda _preduse with the SOC control program in dynamic definite mixed powertrain control device 430 in SOC border, to determine that instantaneous fuel expends coefficient lambda _inst.

Fuel expends coefficient lambda _predsent from predictive controller 420 with dynamic definite SOC border, and transmit by hybrid controller 430, to stablize the dynamic behaviour between driving engine 240, torque machine 230 and ESD210, and preferred engine moment of torsion/speed and motor torque/speed in definite constraint manipulation space, with maximum system efficiency and performance.These constraints (comprising available engine torque, available motor torque, the available power of battery and available hydraulic clutch capacity) are dynamically enforced in each time step by dynamical system control program 430.

Fig. 3 shows and in powertrain control scheme 430, is performed to determine that instantaneous fuel expends coefficient lambda _instexemplary SOC calibration procedure.In the time that current SOC is larger than target SOC, instantaneous fuel expends coefficient lambda _instincrease along with the increase of SOC; At current SOC, than target SOC hour, instantaneous fuel expended coefficient lambda _instreduce along with reducing of SOC.Minimum and maximum charge condition (SOC max and SOC min) dynamically determined in Model Predictive Control scheme 420, and it expends coefficient lambda with preferred fuel _predrelevant.When SOC larger or than minimum SOC hour, instantaneous fuel expends coefficient lambda than maximum SOC _instincrease or reduce with the speed of accelerating.

Operation when the imminent road load (RL) that Fig. 4 A shows wherein prediction does not change.Relevant predict fuel expends coefficient lambda _predkeep not changing, set dynamically definite SOC border (+/-Δ SOC), and the SOC track of the SOC track following of prediction expectation.

The imminent road load (RL) that Fig. 4 B shows wherein prediction at certain some place in future by the operation reducing.The fuel of relevant prediction expends coefficient lambda _predunder the expection of the imminent road load for reducing and correspondingly increase, thereby reduce dynamically definite SOC border (+/-Δ SOC).The SOC track of prediction can reduce with the speed being accelerated with respect to the SOC track of expecting when initial, and the some place that the SOC regaining subsequently reduces at actual road load starts.SOC is regained by the management to power system, comprises and uses fuel cut-off operation, regenerative brake and the control program to ESD210 charging in rolling time domain end.

The imminent road load (RL) that Fig. 4 C shows wherein prediction at following some place by the operation increasing.The fuel of relevant prediction expends coefficient lambda _predunder to the expection of the imminent road load reducing and correspondingly reduce, thereby increase dynamic definite SOC border (+/-Δ SOC).The rate reduction that the SOC track of prediction declines when initial, thus increase with respect to the SOC track of expecting, and the some place that SOC increases at actual road load subsequently starts to reduce.SOC reduces by the management to power system, uses torque machine 230 to produce tractive torque during being included in the road load of increase of rolling time domain end.

Fig. 4 D shows the wherein operation of imminent road load (RL) monotone increasing of prediction.The fuel of relevant prediction expends coefficient lambda _predalong with the imminent road load increasing, correspondingly dullness reduces, thereby has increased dynamic definite SOC border (+/-Δ SOC).The SOC track of prediction is held in the track that connects and hope in the recent period.

Predictive energy management control scheme uses the driving model information of the statistics in vehicle route, imminent road load and rolling time domain control problem, for example, to predict the vehicular drive situation of (within next a few minutes of vehicle operating) in upcoming finite time section.The vehicular drive situation of prediction can be used to the vehicular drive situation of the prediction based in finite time section and predict and regulate vehicle power request.The vehicular drive situation of the prediction in finite time section can be used to predict iteratively the upcoming power request for vehicle propulsion, the predict energy operating strategy preferably using a model in predictive control framework.

It should be understood that and in the scope of the present disclosure, allow to modify.Specifically with reference to preferred embodiment and amendment thereof, the disclosure is described.After reading and having understood application documents, one of ordinary skill in the art will recognize that more amendment and modification.The disclosure is intended to comprise all such amendment and the modification that fall within the scope of it.

Claims (20)

1. one kind for operating the method for the vehicle that has comprised hybrid power system, described hybrid power system comprises the explosive motor of the input link that is operatively connected to hybrid gearbox, comprise can operate in case with the torque machine of energy storing device exchange power, described hybrid gearbox is configured to transmitting torque between described input link, described torque machine and output link, and described method comprises:

Automobile navigation and transit mode that monitoring is relevant to prediction travel path for described vehicle;

Predict the imminent road load relevant with transit mode to described automobile navigation;

Estimate the vehicle propulsion power relevant to the imminent road load of described prediction;

Be identified for the charge condition track of the expectation of described energy storing device;

Prediction is for the charge condition track of described energy storing device, and it is corresponding to the vehicle propulsion power of described estimation with for the charge condition track of the expectation of described energy storing device; With

Control the operation of described hybrid power system in response to the charge condition track of the prediction for described energy storing device.

2. the method for claim 1, further comprises:

Determine the boundary condition relevant to the charge condition track of the prediction for described energy storing device; With

Control the operation of described hybrid power system in response to the output torque of ordering and the described boundary condition relevant to the charge condition track of the prediction for described energy storing device.

3. the method for claim 1, further comprise and carry out load prediction control program, with the prediction imminent road load relevant to automobile navigation and transit mode, thereby estimate the vehicle propulsion power relevant to prediction travel path for described vehicle.

4. method as claimed in claim 3, described load prediction control program is carried out rolling time domain control program, to predict the imminent road load relevant with transit mode to described automobile navigation.

5. method as claimed in claim 4, further comprises execution model predictive control scheme, so that:

Optimize the fuel relevant to described imminent road load and expend coefficient; With

Fuel based on described optimization expends coefficient and dynamically regulates the charge condition track for the prediction of described energy storing device.

6. method as claimed in claim 5, wherein, thereby described fuel expend coefficient optimised make fuel in described rolling time domain expend with total power consumption at least one minimize.

7. method as claimed in claim 6, wherein, described Model Predictive Control scheme comprises the quasi-static model based on power of total hybrid power system loss.

8. the method for claim 1, further comprise and determining at the power stage from described driving engine and from the preferred power division between the power stage of described torque machine, the charge condition track of the prediction of described preferred power division based on for described energy storing device.

9. the method for claim 1, wherein the charge condition track of described expectation comprises electric quantity consumption track.

10. the method for claim 1, wherein the charge condition track of described expectation comprises that electric weight keeps track.

11. 1 kinds for operating the method for the vehicle that has comprised hybrid power system, described hybrid power system comprises the explosive motor of the input link that is operatively connected to hybrid gearbox, comprise can operate in case with the torque machine of energy storing device exchange power, described hybrid gearbox is configured to transmitting torque between described input link, described torque machine and output link, and described method comprises:

Automobile navigation, transit mode and operator's driving model that monitoring is relevant to prediction travel path for described vehicle;

Monitoring is inputted relevant operator's torque request to the operator who is input to for the operator interface of described vehicle;

Prediction and described automobile navigation, transit mode, the imminent road load that operator's driving model is relevant with described operator's torque request;

Estimate the vehicle propulsion power relevant to the imminent road load of described prediction;

Be identified for the charge condition track of the expectation of described energy storing device;

Prediction is for the charge condition track of described energy storing device, and it is corresponding to the vehicle propulsion power of described estimation with for the charge condition track of the expectation of described energy storing device; With

Control the operation of described hybrid power system in response to the charge condition track of the prediction for described energy storing device and described operator's torque request.

12. methods as claimed in claim 11, further comprise:

The fuel of estimating the prediction relevant to the vehicle propulsion power of described estimation expends coefficient; With

Determine to the fuel of described prediction expend coefficient relevant at the power stage from described driving engine with from the preferred power division between the power stage of described torque machine.

13. methods as claimed in claim 12, further comprise:

Determine to power stage from described driving engine and expend coefficient from the relevant instantaneous fuel of the described preferred power division of carrying out between the power stage of described torque machine; With

Wherein, determine that also further to expend coefficient relevant to described instantaneous fuel at the power stage from described driving engine with from the described preferred power division between the power stage of described torque machine.

14. 1 kinds for operating the method for the vehicle that has comprised hybrid power system, comprising:

Predict the travel path of described vehicle;

Monitor automobile navigation and the transit mode relevant to the travel path of described prediction;

Predict the imminent road load relevant with transit mode to described automobile navigation, described automobile navigation is relevant to the travel path of described prediction with transit mode;

Estimate the vehicle propulsion power relevant to the imminent road load of described prediction;

Determine the charge condition track of the expectation of the energy storing device that is configured to the power that is provided for vehicle propulsion;

Prediction is for the charge condition track of described energy storing device, and it is corresponding to the vehicle propulsion power of described estimation with for the charge condition track of the expectation of described energy storing device; With

Control the operation of described hybrid power system in response to the charge condition track of the prediction of described energy storing device.

15. methods as claimed in claim 14, further comprise:

Determine the boundary condition relevant to the charge condition track of the prediction of described energy storing device; With

Control the operation of described hybrid power system in response to the charge condition track of output torque, the described prediction of order and the described boundary condition relevant to the charge condition track of the prediction of described energy storing device.

16. methods as claimed in claim 14, further comprise:

Carry out load prediction control program, to predict the described imminent road load relevant with transit mode to described automobile navigation, so that the estimation described vehicle propulsion power relevant to prediction travel path for described vehicle.

17. methods as claimed in claim 16, wherein,

Described load prediction control program is carried out rolling time domain control program, to predict the described imminent road load relevant with transit mode to described automobile navigation.

18. methods as claimed in claim 17, further comprise execution model predictive control scheme, so that:

Optimize the fuel relevant to described imminent road load and expend coefficient; With

Fuel based on described optimization expends coefficient dynamically adjusts the charge condition track of the prediction of described energy storing device.

19. methods as claimed in claim 14, wherein, the charge condition track of described expectation comprises electric quantity consumption track.

20. methods as claimed in claim 14, wherein, the charge condition track of described expectation comprises that electric weight keeps track.